Machine Learning Engineer

About us:

At Dabster, we are your one-stop destination for talent acquisition and digital innovation. Our customized, scalable talent solutions empower organizations to concentrate on their core business while we expertly match the right talent to the right roles.

Who will you work with:

Partnering with a global technology leader that is driving innovation across cloud, data, AI, and enterprise solutions. They offer an exciting environment for professionals who want to contribute to impactful digital transformation projects and work on cutting-edge technology initiatives.

About the role:

We’re looking for an experienced applied ML engineer to build, validate, and ship ML models and LLM-based components into production. You’ll own ML-powered systems end-to-end — from problem framing through deployment, monitoring, and ongoing operation — and raise the bar for engineering and ML reliability across the team.

What you’ll do:

• Build and deploy production ML models: rankers (learning-to-rank, GBDT), peer-network embeddings/similarity, LLM output normalization, and confidence calibration.
• Integrate ML and LLM components into decoupled, asynchronous pipelines and multi-agent graphs that are reliable, observable, and safe under concurrent load.
• Establish evaluation, monitoring, and data-quality discipline: offline metrics that predict online behavior, drift detection, and reconciliation of projected vs. real impact.
• Build fast, reproducible cloud data pipelines using managed services; automate data movement, cleaning, and preparation.
• Choose simpler solutions when appropriate (rules-based scorers, baselines) and justify when to add ML using evidence.
• Ship thin first versions, iterate quickly with real data/users, and measure impact.
• Design for failure: identify silent-fail modes, build guardrails (including prompt-injection protections), and own the risk register.
• Mentor teammates, improve code quality and architecture, and set technical direction as prototypes become products.

Minimum qualifications:

• 5+ years applied ML or closely related engineering experience.
• Production-proven ML depth: able to map business problems to ML formulations, select methods, and reason about failure modes (leakage, drift, offline/online divergence).
• Track record of shipping models that served real traffic and clients.
• End-to-end ownership of at least one ML-powered product in production.
• Strong Python skills and experience with the ML/data stack, including LLM/agent orchestration, RAG, vector DBs, and AWS.
• Experience building reproducible data pipelines using managed cloud services (avoid one-off scripts).
• Strong software-engineering fundamentals; able to write production-quality code and own a service.
• Comfortable starting with ambiguity and biased toward iterative delivery.
• Experience using AI tools (ChatGPT, Copilot, Claude, etc.) to accelerate work.

How to apply:

If your expertise meets the above job, we would love to hear back from you, kindly share your resume to [email protected]